Toggle Main Menu Toggle Search

Open Access padlockePrints

Inspection-oriented coding service based on machine learning and semantics mining

Lookup NU author(s): Christopher Laing

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


Abstract

HS codes have been adopted by the majority of countries as being the basis for import and export inspection and the generation of trade statistics. Customs authorities and international traders need a HS code query tool to make their processing efficient and automatic. Since HS codes are identified at 5-7 levels of classification, then any intelligent coding service will need to combine a knowledge database, with the techniques of data mining, machine learning and semantics reasoning. In this paper, the authors propose a comprehensive solution for such a coding service. The architecture, related techniques, technical solution and implementation considerations for the proposed system have been provided. Several of the proposed functions and implementation techniques have been developed and deployed by the Shanghai International Airport Entry-Exit Inspection and Quarantine Bureau. The coding service has been published as a Web service, and has the potential to be widely used by authorities and international traders around the world. The proposed system may also be appropriate for other applications that relate to code or classification processes, such as RFID-based or product ontology based applications. (c) 2006 Elsevier Ltd. All rights reserved.


Publication metadata

Author(s): Li YS, Ma ZX, Me W, Laing C

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Expert Systems with Applications: 9th International Conference on Computer Supported Cooperative Work in Design

Year of Conference: 2006

Pages: 835-848

ISSN: 0957-4174

Publisher: Pergamon

DOI: 10.1016/j.eswa.2006.01.019

Library holdings: Search Newcastle University Library for this item

ISBN: 18736793


Share